Impairments identified by comprehensive geriatric assessment in potential candidates for left ventricular assist device and heart transplantation
Bibliographic record
Abstract
Background: The aim of this study was to assess the prevalence of frailty and other impairments in potential left ventricular assist device (LVAD) and heart transplantation (HTx) candidates by performing a preoperative comprehensive geriatric assessment (CGA) and reviewing the treatment recommendations resulting from the CGA. Methods and results: This cross-sectional study included 73 patients aged ≥40 years who received a CGA as part of the patient selection procedure for LVAD and HTx. In every patient, a conclusion comprising frailty and other impairments was formulated based on the medical, mental, functional, and social domains and recommendations were made. The mean age was 58 years (range 40-71) and 70 % were male. In 97 % of patients, at least one impairment was identified by the CGA. The most common impairments were polypharmacy, high morbidity burden, reduced renal function, osteopenia, depression, poor quality of life, reduced functionality, (risk of) malnutrition, reduced grip strength and high caregiver burden. A small proportion of the potential LVAD and HTx candidates were frail (7 % according to Fried's frailty criteria, 6 % according to the Edmonton Frail Scale) and 39 % were pre-frail. The domains for which most impairments were found and the domains for which most treatment recommendations were given matched well, with the functional domain as the frontrunner. Conclusion: This study showed that most of the potential candidates for LVAD or HTx have impairments on at least one domain of the CGA. Impairments and associated risks can contribute to the decision making process for candidacy for LVAD and HTx.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".